The Effect of a Short-Term High-Intensity Circuit Training Program on Work Capacity, Body Composition, and Blood Profiles in Sedentary Obese Men: A Pilot Study
Bibliographic record
Abstract
The objective of this study was to determine how a high-intensity circuit-training (HICT) program affects key physiological health markers in sedentary obese men. Eight obese (body fat percentage >26%) males completed a four-week HICT program, consisting of three 30-minute exercise sessions per week, for a total of 6 hours of exercise. Participants' heart rate (HR), blood pressure (BP), rating of perceived exertion, total work (TW), and time to completion were measured each exercise session, body composition was measured before and after HICT, and fasting blood samples were measured before throughout, and after HICT program. Blood sample measurements included total cholesterol, triacylglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, glucose, and insulin. Data were analyzed by paired t-tests and one-way ANOVA with repeated measures. Statistical significance was set to P < 0.05. Data analyses revealed significant (P < 0.05) improvements in resting HR (16% decrease), systolic BP (5.5% decrease), TW (50.7%), fat tissue percentage (3.6%), lean muscle tissue percentage (2%), cholesterol (13%), triacylglycerol (37%), and insulin (18%) levels from before to after HICT program. Overall, sedentary obese males experienced a significant improvement in biochemical, physical, and body composition characteristics from a HICT program that was only 6 hours of the total exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".